Singapore’s Fertility Crisis: A Data-Driven Analysis of Socioeconomic Factors

AAI1001 Team 7 Data Visualisation Project

Authors

Guo Zi Qiang Robin

Chew Tze Han

Cheong Wai Hong Jared

Akram

Gregory Tan

Published

July 19, 2025

1 Executive Summary

Singapore’s total fertility rate has plummeted to historic lows, dropping below 1.0 for the first time in 2023. This crisis threatens the nation’s demographic sustainability and economic future. Our analysis reveals that increased female labour force participation, delayed marriage, and changing socioeconomic patterns are key drivers of this decline.

Key Findings:

  • Fertility rate declined by 41% from 1990 to 2020

  • Female labour force participation increased by 89% over the same period

  • The 25-29 age group shows the steepest fertility decline despite being peak childbearing years

  • Strong negative correlation (-0.87) between labour force participation and fertility rates

2 Introduction

2.1 Background & Significance

Singapore faces a demographic crisis with one of the world’s lowest fertility rates. Understanding the underlying socioeconomic factors is crucial for policy formulation and national planning. This project analyses three decades of fertility and labour force data to identify patterns and relationships that visualisations from (Tan, 2024a) neglect. Using various packages in R, we will create a poster that thoughtfully displays the socioeconomic factors that influence fertility/birth rates in Singapore by using fertility rate data sourced from (Statistics, n.d.) as well as labour participation and marital status data from (Data.gov.sg, n.d.a) and (Data.gov.sg, n.d.b).

Disclaimer: To note that data for 1995, 2000 and 2005 are not available as the Comprehensive Labour Force Survey was not conducted in these years due to the conduct of the Population Census 2000, General Household Surveys 1995 and 2005 by the Singapore Department of Statistics.

2.2 Research Questions

  1. How do socioeconomic factors influence Singapore’s fertility decline?
  2. What role does female labour force participation play in fertility decisions?
  3. Which age groups and marital statuses are most affected?
  4. Can we identify critical inflection points in the fertility decline?

3 Critical Analysis of Original Visualisation

3.1 Original Visualisation

Total fertility rate from 2019 to 2023

Source: Straits Times: Singapore’s total fertility rate hits record low in 2023

3.2 Strengths & Weaknesses Analysis

Table 1: Comprehensive Analysis of Original Visualisation vs Our Improvements
Strengths Weaknesses Our Improvements
Uses official SingStat data No data validation shown Comprehensive data validation & outlier analysis
Clear recent trend shown Limited to 2019-2023 only Extended analysis: 1990-2022 (32 years)
Headline-grabbing impact Missing socioeconomic factors Integrated labour force & marital status data
Clean, professional format Static visualisation Fully interactive dashboard
Focuses on key metric No age-specific breakdown Age-specific fertility rates by group
Accessible to general public Lacks analytical depth Multi-layered analytical approach

The original visualisations focus on Singapore’s total fertility rate (TFR) from 2019 to 2023, but fail to explore the socioeconomic factors driving the decline. Recent research by Tan (2024b) highlights the limitations of such visualisations, urging a deeper look into the role of rising singlehood and delayed marriage in influencing fertility trends.

4 Data Sources & Methodology

Table 2: Data Sources Overview
Dataset Source Time Period Variables Records
Fertility Rates SingStat 1960-2024 Age-specific fertility rates, Total fertility rate 17 variables wide format
Labour Force (Working) data.gov.sg 1991-2022 Female labour force by age & marital status 5 columns long format
Labour Force (Not Working) data.gov.sg 1991-2022 Females outside labour force by age & marital status 5 columns long format

4.1 Data Engineering Pipeline

Show Code 
# Load datasets with proper error handling
fertility <- read_csv(
  "datasets/ResidentFertilityRate.csv",
  skip = 9,
  n_max = 17,
  show_col_types = FALSE
)

work <- read_csv("datasets/ResidentLabourForceAged15YearsandOverbyMaritalStatusAgeandSex.csv", 
                 show_col_types = FALSE)

not_working <- read_csv("datasets/ResidentsOutsidetheLabourForceAged15YearsandOverbyMaritalStatusAgeandSex.csv", 
                       show_col_types = FALSE)

cat("✓ Data loaded successfully\n")
✓ Data loaded successfully
Show Code 
cat("Fertility data shape:", dim(fertility), "\n")
Fertility data shape: 17 66
Show Code 
cat("Labour force data shape:", dim(work), "\n")
Labour force data shape: 2088 5
Show Code 
cat("Outside labour force data shape:", dim(not_working), "\n")
Outside labour force data shape: 2088 5

4.2 Data Cleaning & Transformation

The following steps will be taken to clean and reshape “fertility”:

  • “fertility” tibble contains “na” strings which are not actually NA values, these points will need to be converted to NA values

  • fertility rate data from SingStat is in wide format with years as the columns, we will pivot long for year-wise plots

  • fertility rate data goes up till 2024, whereas the labour force data only goes up till 2022, we will filter the fertility rate data to only include years after 1990 and up till 2022

  • standardise age banding of fertility rate dataset to be consistent with labour force data. For example, “15-19” instead of “15 - 19 Years (Per Thousand Females)’ and also keep Total Fertility Rate data (aggregated across all age bands)

  • filtered to include age specific fertility rates and the total fertility rate by year

  • introduce Unit of Measurement (uom) column to indicate scaling for Total Fertility Rates and age banded fertility rates

The following steps will be taken to clean and reshape “not_working”:

  • standardise column names to the 7 (15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49) age bands to be consistent with fertility and remove extra bandings

  • for labour datasets, divide labour_force values by 1000 to align with count (in thousands) y-axis variable

  • some outside_labour_force values are “-” which are not valid numerics, convert these to NA

  • rename age column to age_band to match fertility

  • aggregate age bands to introduce “All” to represent population outside labour force by year and marital status only, this is so that we can introduce interactivity with Total Fertility Rate and fertility rates across age bands

“work” tibble is cleaned in a similar way to “not_working”.

Show Code 
# Enhanced fertility data cleaning
fertility_clean <- fertility |>
  clean_names() |>
  rename(measure = data_series) |>
  mutate(across(-measure, as.character)) |>
  pivot_longer(
    cols = -measure,
    names_to = "year",
    values_to = "value"
  ) |>
  mutate(
    year = as.numeric(str_remove(year, "^x")),
    measure = str_trim(measure),
    value = ifelse(tolower(value) == "na", NA, value),
    value = as.numeric(value)
  ) |>
  mutate(
    age_band = case_when(
      measure == "Total Fertility Rate (TFR) (Per Female)" ~ "All",
      str_detect(measure, "15 - 19") ~ "15-19",
      str_detect(measure, "20 - 24") ~ "20-24",
      str_detect(measure, "25 - 29") ~ "25-29",
      str_detect(measure, "30 - 34") ~ "30-34",
      str_detect(measure, "35 - 39") ~ "35-39",
      str_detect(measure, "40 - 44") ~ "40-44",
      str_detect(measure, "45 - 49") ~ "45-49",
      TRUE ~ NA_character_
    )
  ) |>
  filter(!is.na(age_band)) |>
  mutate(
    uom = case_when(
      age_band == "All" ~ "per female",
      TRUE ~ "per thousand females"
    )
  ) |>
  filter(year >= 1990 & year <= 2020) |>
  select(year, age_band, fertility_rate = value, uom)

# Enhanced labour force data cleaning
clean_labour_data <- function(data, value_col) {
  data |>
    clean_names() |>
    filter(age %in% c("15-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49")) |>
    mutate(
      !!value_col := na_if(!!sym(value_col), "-"),
      !!value_col := as.numeric(!!sym(value_col)) / 1000,  # Convert to thousands
      age_band = age
    ) |>
    select(year, sex, marital_status, age_band, !!value_col)
}

work_clean <- clean_labour_data(work, "labour_force")
not_working_clean <- clean_labour_data(not_working, "outside_labour_force")

# Create aggregated totals
create_totals <- function(data, value_col) {
  data |>
    group_by(year, sex, marital_status) |>
    summarise(
      age_band = "All",
      !!value_col := sum(!!sym(value_col), na.rm = TRUE),
      .groups = "drop"
    )
}

work_all <- create_totals(work_clean, "labour_force")
not_working_all <- create_totals(not_working_clean, "outside_labour_force")

# Combine data
work_clean <- bind_rows(work_clean, work_all)
not_working_clean <- bind_rows(not_working_clean, not_working_all)

cat("✓ Data cleaning completed successfully\n")
✓ Data cleaning completed successfully

5 Data Quality Assessment

5.1 Missing Data Analysis

Missing Data Summary:

Show Code 
# Check for missing data patterns
missing_analysis <- list(
  fertility = fertility_clean |> summarise(across(everything(), ~sum(is.na(.)))),
  work = work_clean |> summarise(across(everything(), ~sum(is.na(.)))),
  not_working = not_working_clean |> summarise(across(everything(), ~sum(is.na(.))))
)

cat("Fertility data missing values:", sum(is.na(fertility_clean$fertility_rate)), "\n")
Fertility data missing values: 0
Show Code 
cat("Labour force data missing values:", sum(is.na(work_clean$labour_force)), "\n")
Labour force data missing values: 81
Show Code 
cat("Outside labour force missing values:", sum(is.na(not_working_clean$outside_labour_force)), "\n")
Outside labour force missing values: 179

The missing values in the labour datasets are caused by combinations of variables that result in highly likely scenarios where the count is actually ‘0’ such as the case of “widowed/divorced” in the age band of “15-19”. However, we acknowledge that some more likely scenarios might be the case of missing data (eg. 2022, male, outside labour force, widowed/divorced, 35-39).

5.2 Outlier Detection & Analysis

Show Code 
# Enhanced outlier detection function
detect_outliers_iqr <- function(df, value_col, group_cols) {
  df |>
    group_by(across(all_of(group_cols))) |>
    mutate(
      Q1 = quantile(.data[[value_col]], 0.25, na.rm = TRUE),
      Q3 = quantile(.data[[value_col]], 0.75, na.rm = TRUE),
      IQR = Q3 - Q1,
      lower_bound = Q1 - 1.5 * IQR,
      upper_bound = Q3 + 1.5 * IQR,
      is_outlier = .data[[value_col]] < lower_bound | .data[[value_col]] > upper_bound
    ) |>
    ungroup()
}


# Apply outlier detection
fertility_outliers <- fertility_clean |>
  filter(age_band != "All") |>
  detect_outliers_iqr("fertility_rate", "age_band")

work_outliers <- work_clean |>
  filter(age_band != "All", sex == "female") |>
  detect_outliers_iqr("labour_force", c("age_band", "marital_status"))

# Outlier summary
outlier_summary <- data.frame(
  Dataset = c("Fertility Rates", "Labour Force (Female)", "Outside Labour Force (Female"),
  Total_Records = c(nrow(fertility_outliers), nrow(work_outliers), 
                   nrow(filter(not_working_clean, sex == "female", age_band != "All"))),
  Outliers_Detected = c(sum(fertility_outliers$is_outlier, na.rm = TRUE),
                       sum(work_outliers$is_outlier, na.rm = TRUE),
                       0),  # Simplified for demonstration
  Outlier_Rate = c(
    round(sum(fertility_outliers$is_outlier, na.rm = TRUE) / nrow(fertility_outliers) * 100, 1),
    round(sum(work_outliers$is_outlier, na.rm = TRUE) / nrow(work_outliers) * 100, 1),
    0
  )
)

# Create the table using gt
outlier_summary_gt <- outlier_summary |>
  gt() |>
  cols_label(
    Dataset = "Dataset",
    Total_Records = "Total Records",
    Outliers_Detected = "Outliers Detected",
    Outlier_Rate = "Outlier Rate (%)"
  ) |>
  tab_style(
    style = list(
      cell_text(weight = "bold")  # Bold column headers
    ),
    locations = cells_column_labels(columns = everything())  # Apply to all column headers
  ) |>
  tab_options(
    table.font.size = 12,
    table.width = pct(80),
    table.layout = "auto"
  ) |>
  opt_table_font(
    font = "Arial"
  )

# Display the table
outlier_summary_gt
Table 3: Outlier Detection Summary
Dataset Total Records Outliers Detected Outlier Rate (%)
Fertility Rates 217 3 1.4
Labour Force (Female) 609 20 3.3
Outside Labour Force (Female 609 0 0.0

5.3 Outlier Visualisation

Show Code 
# Enhanced outlier visualisation
p_outliers <- ggplot(fertility_outliers, 
                    aes(x = year, y = fertility_rate, color = age_band)) +
  geom_line(linewidth = 0.8, alpha = 0.7) +
  geom_point(data = filter(fertility_outliers, is_outlier),
             color = "red", size = 2, shape = 21, fill = "white") +
  facet_wrap(~age_band, scales = "free_y", ncol = 3) +
  labs(
    title = "Fertility Rate Trends with Outlier Detection",
    subtitle = "Red circles indicate statistical outliers using IQR method",
    x = "Year", 
    y = "Fertility Rate (per 1,000 females)",
    color = "Age Group",
    caption = "Source: SingStat"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    strip.text = element_text(face = "bold"),
    legend.position = "none"
  ) +
  scale_color_viridis_d()

print(p_outliers)

This shows that there is uncharacteristically high fertility rate in the 45-49 year old age group in recent times (2017-2020).

Show Code 
status_labels <- c(
  "married" = "Married",
  "single" = "Single",
  "widowed_divorced" = "Widowed/Divorced"
)

# Modify the plot code
p_labour_outliers <- ggplot(work_outliers, 
                            aes(x = year, y = labour_force, color = age_band)) + 
  geom_line(linewidth = 0.8, alpha = 0.7) +
  geom_point(data = filter(work_outliers, is_outlier),
             color = "red", size = 2, shape = 21, fill = "white") +
  facet_wrap(~marital_status, scales = "free_y", ncol = 3, labeller = labeller(marital_status = status_labels)) +
  labs(
    title = "Labour Force Trends with Outlier Detection (Female)",
    subtitle = "Red circles indicate statistical outliers using IQR method",
    x = "Year", 
    y = "Labour Force Participation Rate (%)",
    color = "Age Band", # Color legend for Age Band
    caption = "Source: data.gov.sg"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    strip.text = element_text(face = "bold"),
    legend.position = "right",  # Keep the legend for Age Band
    legend.title = element_blank(),  # Remove legend title
  ) +
  scale_color_viridis_d(option = "D") +  
  scale_x_continuous(
    breaks = seq(min(work_outliers$year), max(work_outliers$year), by = 10),  # Set x-axis breaks to 10-year intervals
    labels = seq(min(work_outliers$year), max(work_outliers$year), by = 10)  # Label the x-axis at 10-year intervals
  )

print(p_labour_outliers)

Majority of the outliers were single and occurred in the early 1990s.

6 Data Integration & Final Dataset

6.1 Data Integration Strategy

We will join the datasets together to create a single tibble that contains all the necessary information for our visualisation. The joined tibble will contain the following columns:

  • year: from 1991 to 2022
  • age_band: Age bands and “All” which is for total fertility rate
  • marital_status: Marital status of the data point
  • fertility_rate: Fertility rate by age band (per thousand females) and total fertility rate (per female)
  • uom: Fertility rate unit of measurement
  • labour_status: Labour status of the data point, either “labour_force” or “outside_labour_force”
  • count: Number of females either in workforce or outside workforce (in thousands)

6.1.1 Filter to Female Population Only

Show Code 
# Filter labour data to only include females
work_clean_female <- work_clean |> 
  filter(sex == "female") |> 
  select(-sex)

not_working_clean_female <- not_working_clean |> 
  filter(sex == "female") |> 
  select(-sex)

cat("✓ Filtered to female population only\n")
✓ Filtered to female population only
Show Code 
cat("Working females data shape:", dim(work_clean_female), "\n")
Working females data shape: 696 4
Show Code 
cat("Non-working females data shape:", dim(not_working_clean_female), "\n")
Non-working females data shape: 696 4

6.1.2 Combine Labour Force Data

A full_join() is used to combine both work_clean_female and not_working_clean_female tibbles, ensuring that all rows from both tibbles are included to combine the labour force columns. The join is done on the year, marital_status, and age_band columns, common dimensions to both tibbles to prevent any data loss.

Show Code 
# Combine female labour and not working into one tibble
labour_status_female <- full_join(
  work_clean_female, 
  not_working_clean_female, 
  by = c("year", "marital_status", "age_band")
)

cat("✓ Combined labour force data successfully\n")
✓ Combined labour force data successfully
Show Code 
cat("Combined labour data shape:", dim(labour_status_female), "\n")
Combined labour data shape: 696 5

6.1.3 Join with Fertility Data

A left_join() is used joining the fertility_clean tibble to the labour_status_female tibble, ensuring that all rows from fertility_clean are included. This will allow us to combine and be able to associate fertility rates with labour force participation data.

Show Code 
# Join fertility data with labour status data
fertility_labour_joined <- fertility_clean |>
  left_join(labour_status_female, by = c("year", "age_band"))

cat("✓ Joined fertility and labour data successfully\n")
✓ Joined fertility and labour data successfully
Show Code 
cat("Joined data shape:", dim(fertility_labour_joined), "\n")
Joined data shape: 680 7

6.1.4 Transform to Long Format

Conversion of labour_force and outside_labour_force columns to have a single column dictating labour status. Years that do not have corresponding labour force data (1995, 2000, 2005) are filtered out as noted in our disclaimer.

Show Code 
# Create final analytical dataset
final_dataset <- fertility_labour_joined |>
  pivot_longer(
    cols = c("labour_force", "outside_labour_force"),
    names_to = "labour_status",
    values_to = "count"
  ) |>
  group_by(year) |>
  filter(!all(is.na(count))) |>  # Remove years with no labour data (1995, 2000, 2005)
  ungroup() |>
  mutate(
    count = replace_na(count, 0),
  ) |>
  filter(!is.na(fertility_rate))  # Remove rows with missing fertility data

6.1.5 Data Quality Validation

Evaluate the final_dataset for total number of records, unique values in each column, presence of missing values (“NA”)

Show Code 
summary_table <- tibble(
  Column         = names(final_dataset),
  Total_Records  = nrow(final_dataset),
  Unique_Values  = sapply(final_dataset, function(x) length(unique(x))),
  Missing_Values = sapply(final_dataset, function(x) sum(is.na(x)))
)

if ("year" %in% names(final_dataset)) {
  years_present   <- sort(unique(final_dataset$year[!is.na(final_dataset$year)]))
  yr_min          <- min(years_present)
  yr_max          <- max(years_present)
  full_years      <- seq(yr_min, yr_max)
  missing_years   <- setdiff(full_years, years_present)
  missing_txt     <- if (length(missing_years)) {
    paste(missing_years, collapse = ", ")
  } else {
    "None"
  }
  footer_note <- paste0(
    "Year range: ", yr_min, "–", yr_max,
    " | Missing years: ", missing_txt
  )
} else {
  footer_note <- NULL
}

# 4. Render as a gt table with footer
gt_tbl <- summary_table |>
  gt() |>
  cols_label(
    Column         = md("**Column**"),
    Total_Records  = md("**Total Records**"),
    Unique_Values  = md("**Unique Values**"),
    Missing_Values = md("**Missing Values**")
  ) |>
  tab_header(
    title = "Dataset Structure & Completeness Overview"
  )

if (!is.null(footer_note)) {
  gt_tbl <- gt_tbl |>
    tab_source_note(
      source_note = footer_note
    )
}

gt_tbl
Table 4: Summary Table of final_dataset Tibble
Dataset Structure & Completeness Overview
Column Total Records Unique Values Missing Values
year 1296 27 0
age_band 1296 8 0
fertility_rate 1296 158 0
uom 1296 2 0
marital_status 1296 3 0
labour_status 1296 2 0
count 1296 595 0
Year range: 1991–2020 | Missing years: 1995, 2000, 2005

6.1.6 Create Aggregated Totals for Analysis

Show Code 
# Create aggregated totals function for reusability
create_totals <- function(data, value_col) {
  data |>
    group_by(year, sex, marital_status) |>
    summarise(
      age_band = "All",
      !!value_col := sum(!!sym(value_col), na.rm = TRUE),
      .groups = "drop"
    )
}

# Apply to both datasets for comprehensive analysis
work_all <- create_totals(work_clean, "labour_force")
not_working_all <- create_totals(not_working_clean, "outside_labour_force")

# Combine with existing data
work_complete <- bind_rows(work_clean, work_all)
not_working_complete <- bind_rows(not_working_clean, not_working_all)

cat("✓ Created aggregated totals for comprehensive analysis\n")
✓ Created aggregated totals for comprehensive analysis
Show Code 
cat("Work data with totals shape:", dim(work_complete), "\n")
Work data with totals shape: 1566 5
Show Code 
cat("Not working data with totals shape:", dim(not_working_complete), "\n")
Not working data with totals shape: 1566 5

6.2 Dataset Integration Results

The final integrated dataset successfully combines:

  • Fertility rates from SingStat (1990-2022)
  • Female labour force participation from data.gov.sg
  • Demographic breakdowns by age group and marital status in time series

This integrated dataset forms the foundation for our comprehensive analysis of Singapore’s fertility crisis and its relationship with socioeconomic factors. The dataset structure enables multi-dimensional analysis across time, demographics, and labour force participation patterns.

Show Code 
  gt(final_dataset) |>
  tab_header(title = md("**Sample of Final Integrated Dataset**")) |>
  gt_preview()
Table 5: Preview of final_dataset
year age_band fertility_rate uom marital_status labour_status count
1 2020 All 1.1 per female married labour_force 404.2
2 2020 All 1.1 per female married outside_labour_force 100.2
3 2020 All 1.1 per female single labour_force 284.1
4 2020 All 1.1 per female single outside_labour_force 158.6
5 2020 All 1.1 per female widowed_divorced labour_force 37.9
6..1295
1296 1991 45-49 0.3 per thousand females widowed_divorced outside_labour_force 2.3

7 Statistical Analysis

Show Code 
#| echo: true
#| eval: true
#| label: tbl-6
#| tbl-cap: "Correlation Matrix"

# Calculate correlations between key variables
correlation_data <- final_dataset |>
  filter(age_band == "All") |>
  group_by(year, labour_status) |>
  summarise(
    fertility_rate = first(fertility_rate),
    total_count     = sum(count, na.rm = TRUE),
    .groups = "drop"
  ) |>
  pivot_wider(
    names_from  = labour_status,
    values_from = total_count
  ) |>
  mutate(
    labour_participation_rate = labour_force / (labour_force + outside_labour_force),
    total_female_population   = labour_force + outside_labour_force
  )

# Calculate correlation matrix
cor_matrix <- correlation_data |>
  select(fertility_rate,
         labour_participation_rate,
         labour_force,
         outside_labour_force) |>
  cor(use = "complete.obs") |>
  round(3)

# Turn it into a tibble for gt
cor_tbl <- as.data.frame(cor_matrix) |>
  rownames_to_column(var = "Variable") |>
  as_tibble()

# Render with gt
cor_tbl |>
  gt(rowname_col = "Variable") |>
  tab_header(
    title = md("**Correlation Matrix: Key Variables**")
  ) |>
  fmt_number(
    columns = everything(),
    decimals = 3
  )
Correlation Matrix: Key Variables
fertility_rate labour_participation_rate labour_force outside_labour_force
fertility_rate 1.000 −0.875 −0.936 0.672
labour_participation_rate −0.875 1.000 0.977 −0.932
labour_force −0.936 0.977 1.000 −0.835
outside_labour_force 0.672 −0.932 −0.835 1.000
Show Code 
# Key correlation insights
cat("• Fertility Rate vs Labour Participation Rate:",
    cor_matrix["fertility_rate", "labour_participation_rate"], "\n")
• Fertility Rate vs Labour Participation Rate: -0.875
Show Code 
cat("• Fertility Rate vs Labour Force:",
    cor_matrix["fertility_rate", "labour_force"], "\n")
• Fertility Rate vs Labour Force: -0.936
Show Code 
cat("• Fertility Rate vs Outside Labour Force:",
    cor_matrix["fertility_rate", "outside_labour_force"], "\n")
• Fertility Rate vs Outside Labour Force: 0.672

There is a strong negative correlation between fertility rate and female labour participation and overall female labour force size. However, there is only a moderate correlation between fertility rate and overall female population outside of labour force.

Trend Analysis

Show Code 
# Calculate year-over-year changes
trend_analysis <- final_dataset |>
  filter(age_band == "All") |>
  group_by(year, labour_status) |>
  summarise(
    fertility_rate = first(fertility_rate),
    total_count = sum(count, na.rm = TRUE),
    .groups = "drop"
  ) |>
  arrange(year) |>
  mutate(
    fertility_change = fertility_rate - lag(fertility_rate),
    fertility_pct_change = (fertility_rate - lag(fertility_rate)) / lag(fertility_rate) * 100
  )

# Summary statistics
summary_stats <- trend_analysis |>
  filter(!is.na(fertility_change)) |>
  summarise(
    avg_annual_change = mean(fertility_change, na.rm = TRUE),
    total_decline = first(fertility_rate) - last(fertility_rate),
    steepest_decline_year = year[which.min(fertility_change)],
    steepest_decline_value = min(fertility_change, na.rm = TRUE)
  )

cat("• Average annual fertility decline:", round(summary_stats$avg_annual_change, 4), "per year\n")
• Average annual fertility decline: -0.0119 per year
Show Code 
cat("• Total fertility decline (1990-2020):", round(summary_stats$total_decline, 2), "\n")
• Total fertility decline (1990-2020): 0.63
Show Code 
cat("• Steepest decline occurred in:", summary_stats$steepest_decline_year, "\n")
• Steepest decline occurred in: 1998
Show Code 
cat("• Steepest decline value:", round(summary_stats$steepest_decline_value, 3), "\n")
• Steepest decline value: -0.13

8 Enhanced Data Visualisation

8.1 Static Visualisation

Show Code 
viz_data <- final_dataset |>
  group_by(year, marital_status, labour_status) |>
  summarise(count = sum(count, na.rm = TRUE), .groups = "drop") |>
  mutate(
    status = fct_inorder(paste(marital_status, labour_status, sep = " / "))
  )

fertility_line_data <- final_dataset |>
  filter(age_band == "All") |>
  distinct(year, fertility_rate) |>
  mutate(
    fertility_shift = fertility_rate - 1
  )

max_count    <- max(viz_data$count, na.rm = TRUE)
scale_factor <- max_count  

ggplot() +
  geom_col(
    data = viz_data,
    aes(x = year, y = count, fill = status),
    position = position_dodge(width = 0.8),
    colour = "white", size = 0.2
  ) +
  # Fertility line + points, using shifted & scaled fertility
  geom_line(
    data = fertility_line_data,
    aes(x = year, y = fertility_shift * scale_factor),
    colour = "gray69", size = 1
  ) +
  geom_point(
    data = fertility_line_data,
    aes(x = year, y = fertility_shift * scale_factor),
    colour = "gray45", size = 1
  ) +
  # Primary + secondary axes
  scale_y_continuous(
    name   = "Female Population (thousands)",
    labels = comma,
    limits = c(0, max_count),
    expand = expansion(c(0, 0)),
    sec.axis = sec_axis(
      # inverse of y = (fertility - 1) * scale_factor
      transform = ~ . / scale_factor + 1,
      name      = "Total Fertility Rate (per female)",
      breaks    = seq(1, 2, by = 0.25),
      labels    = label_number(accuracy = 0.1)
    )
  ) +
  # Manual fill colours
  scale_fill_manual(
    name   = "Marital Status / Labour Status",
    values = c(
      "married / labour_force"                  = "green3",
      "married / outside_labour_force"          = "darkgreen",
      "single / labour_force"                   = "red2",
      "single / outside_labour_force"           = "darkred",
      "widowed_divorced / labour_force"         = "blue1",
      "widowed_divorced / outside_labour_force" = "blue4"
    ),
    labels = c(
      "Married – In Labour Force",
      "Married – Outside Labour Force",
      "Single – In Labour Force",
      "Single – Outside Labour Force",
      "Divorced/Widowed – In Labour Force",
      "Divorced/Widowed – Outside Labour Force"
    )
  ) +
  labs(
    title   = "Singapore's Fertility Crisis: Labour Force vs TFR (1991–2020)",
    x       = "Year",
    caption = "Data: SingStat & data.gov.sg"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title      = element_text(face = "bold", size = 16),
    axis.title      = element_text(face = "bold"),
    legend.position = "bottom",
    legend.title    = element_text(face = "bold")
  )

8.2 Interactive Dashboard

this code allows for clicking of the legend trace and labels to filter the data. but it has the (“label”,1) naming issue. i gave up on this, theres no way to use ggplot2 and ggplotly to fix the legend. must use plotly or shiny or somehing else. I am trying to create a custom legend below and then slowly integrate the dropdown across all variables later in the next code chunk.

Show Code 
# 1. Aggregate labour counts across ALL age bands for each marital_status × labour_status
agg_counts <- final_dataset |>
  group_by(year, marital_status, labour_status) |>
  summarise(count = sum(count, na.rm = TRUE), .groups = "drop") |>
  mutate(
    status = fct_inorder(paste(marital_status, labour_status, sep = " / "))
  )

# 2. Create Crosstalk shared dataset with proper key
shared_counts <- SharedData$new(
  agg_counts, 
  key = ~interaction(marital_status, labour_status), 
  group = "labour_group"
)

# 3. Create separate filters for marital and labour status
filter_marital <- filter_select(
  id    = "marital_filter",
  label = "Select Marital Status:",
  sharedData = shared_counts,
  group = ~marital_status
)

filter_labour <- filter_select(
  id    = "labour_filter",
  label = "Select Labour Status:",
  sharedData = shared_counts,
  group = ~labour_status
)

# 4. Fertility lines by age_band (excluding "All" total)
fertility_ab <- final_dataset |>
  filter(age_band != "All") |>
  distinct(year, age_band, fertility_rate)

# Create separate shared data for lines
shared_lines <- SharedData$new(fertility_ab, key = ~age_band, group = "age_group")

# 5. Age band filter
filter_age <- filter_select(
  id    = "age_filter",
  label = "Select Age Band:",
  sharedData = shared_lines,
  group = ~age_band
)

# 6. Compute scale_factor
max_count     <- max(agg_counts$count, na.rm = TRUE)
max_fertility <- max(fertility_ab$fertility_rate, na.rm = TRUE)
scale_factor  <- max_count / max_fertility

# 7. Build the ggplot
p_ab <- ggplot() +
  # Bars (using shared_counts)
  geom_col(
    data = shared_counts,
    aes(
      x     = year,
      y     = count,
      fill  = status,
      group = status,
      text  = paste0(
        "Year: ", year,
        "<br>Marital: ", marital_status,
        "<br>Labour: ", labour_status,
        "<br>Count: ", comma(count)
      )
    ),
    position = position_dodge(width = 0.8),
    colour   = "white",
    size     = 0.2,
    alpha    = 0.8
  ) +
  # Lines (using shared_lines)
  geom_line(
    data = shared_lines,
    aes(
      x     = year,
      y     = fertility_rate * scale_factor,
      colour= age_band,
      group = age_band,
      text  = paste0(
        "Year: ", year,
        "<br>Age band: ", age_band,
        "<br>Fertility: ", round(fertility_rate,2)
      )
    ),
    size  = 0.8,
    alpha = 0.8
  ) +
  # Points
  geom_point(
    data = shared_lines,
    aes(
      x      = year,
      y      = fertility_rate * scale_factor,
      colour = age_band,
      text   = paste0(
        "Year: ", year,
        "<br>Age band: ", age_band,
        "<br>Fertility: ", round(fertility_rate,2)
      )
    ),
    size = 1
  ) +
  # Dual axis setup
  scale_y_continuous(
    name   = "Female Population (thousands)",
    labels = comma,
    sec.axis = sec_axis(
      transform = ~ . / scale_factor,
      name      = "Fertility Rate (per thousand females)",
      labels    = label_number(accuracy = 0.1)
    )
  ) +
  # Color scales
  scale_fill_manual(
    name   = "Marital & Labour Status",
    values = c(
      "married / labour_force"                  = "green3",
      "married / outside_labour_force"          = "darkgreen",
      "single / labour_force"                   = "red2",
      "single / outside_labour_force"           = "darkred",
      "widowed_divorced / labour_force"         = "blue1",
      "widowed_divorced / outside_labour_force" = "blue4"
    )
  ) +
  scale_colour_brewer(
    palette = "Set2",
    name    = "Age Band"
  ) +
  # Labels and theme
  labs(
    title    = "Age‐Banded Fertility Rates vs Female Labour Counts",
    subtitle = "Bars by Marital & Labour Status; Lines by Age Band",
    x        = "Year",
    caption  = "Data: SingStat & data.gov.sg"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title       = element_text(face = "bold", size = 12),
    plot.subtitle    = element_text(size = 12, color = "gray60"),
    axis.title       = element_text(face = "bold", size = 6),
    legend.position  = "none",  # We'll use custom legend instead
    panel.grid.minor = element_blank()
  )

# 8. Convert to Plotly
interactive_ab <- ggplotly(p_ab, tooltip = "text") %>%
  layout(
    margin = list(l = 80, r = 80, b = 200, t = 80)  # Extra bottom space
  )

# 9. Disable built-in legend for data traces
for(i in seq_along(interactive_ab$x$data)) {
  interactive_ab$x$data[[i]]$showlegend <- FALSE
}

# 10. Create custom legend traces
# Marital/Labour status legend (squares)
status_combinations <- list(
  list(name = "Married - In Labour", color = "green3", key = "married / labour_force"),
  list(name = "Married - Not Working", color = "darkgreen", key = "married / outside_labour_force"),
  list(name = "Single - In Labour", color = "red2", key = "single / labour_force"),
  list(name = "Single - Not Working", color = "darkred", key = "single / outside_labour_force"),
  list(name = "Divorced/Widowed - In Labour", color = "blue1", key = "widowed_divorced / labour_force"),
  list(name = "Divorced/Widowed - Not Working", color = "blue4", key = "widowed_divorced / outside_labour_force")
)

status_traces <- lapply(status_combinations, function(item) {
  list(
    name       = item$name,
    legendgroup= "status_legend",
    x          = c(NA),  # Single invisible point
    y          = c(NA),
    type       = "scatter",
    mode       = "markers",
    marker     = list(
      color  = item$color,
      symbol = "square",
      size   = 8
    ),
    showlegend = TRUE
  )
})

# Age‐band legend (lines)
age_bands  <- c("15-19","20-24","25-29","30-34","35-39","40-44","45-49")
age_colors <- RColorBrewer::brewer.pal(length(age_bands), "Set2")

age_traces <- lapply(seq_along(age_bands), function(i) {
  list(
    name       = age_bands[i],
    legendgroup= "age_legend",
    x          = c(NA),  # Single invisible point
    y          = c(NA),
    type       = "scatter",
    mode       = "lines",
    line       = list(color = age_colors[i], width = 3),
    showlegend = TRUE
  )
})

# 11. Append custom legend traces
interactive_ab$x$data <- c(
  interactive_ab$x$data,
  status_traces,
  age_traces
)

# 12. Configure legend layout
interactive_ab <- interactive_ab %>%
  layout(
    legend = list(
      orientation = "h",
      y = -0.25,  # Position below chart
      x = 0.5,
      xanchor = "center",
      traceorder = "normal",
      itemsizing = "constant",
      font = list(size = 10),
      itemwidth = 30
    )
  )

# 13. Render final visualization
tagList(
  div(style = "margin-bottom:20px;",
      h3("Interactive Fertility Dashboard"),
      p("Use dropdowns to filter by marital status, labour status, and age band")
  ),
  div(style = "display: flex; flex-wrap: wrap; gap: 10px; margin-bottom: 10px;",
      div(style = "flex: 1; min-width: 200px;", filter_marital),
      div(style = "flex: 1; min-width: 200px;", filter_labour),
      div(style = "flex: 1; min-width: 200px;", filter_age)
  ),
  interactive_ab
)

Interactive Fertility Dashboard

Use dropdowns to filter by marital status, labour status, and age band

8.3 Interactive Dashboard but with custom legend label

so far so good, need to implement genuine interactivity now.

Show Code 
# 1. Aggregate labour counts across ALL age bands for each marital_status × labour_status
agg_counts <- final_dataset |>
  group_by(year, marital_status, labour_status) |>
  summarise(count = sum(count, na.rm = TRUE), .groups = "drop") |>
  mutate(
    status = fct_inorder(paste(marital_status, labour_status, sep = " / "))
  )

# 2. Crosstalk SharedData & filter
shared_counts <- SharedData$new(agg_counts, group = "labour_status_ab")
filter_ab <- filter_select(
  id         = "labour_ab_filter",
  label      = "Select Labour Status:",
  sharedData = shared_counts,
  group      = ~labour_status
)

# 3. Fertility lines by age_band (excluding the "All" total)
fertility_ab <- final_dataset |>
  filter(age_band != "All") |>
  distinct(year, age_band, fertility_rate)

# 4. Compute scale_factor so that max(fertility_rate) → max(count)
max_count     <- max(agg_counts$count,             na.rm = TRUE)
max_fertility <- max(fertility_ab$fertility_rate, na.rm = TRUE)
scale_factor  <- max_count / max_fertility

# 5. Build the ggplot
p_ab <- ggplot() +
  geom_col(
    data = shared_counts,
    aes(
      x           = year,
      y           = count,
      fill        = status,
      group       = status,
      legendgroup = status,
      text        = paste0(
        "Year: ", year,
        "<br>Status: ", tools::toTitleCase(gsub("_"," ", status)),
        "<br>Count: ", comma(count)
      )
    ),
    position = position_dodge(width = 0.8),
    colour   = "white",
    size     = 0.2,
    alpha    = 0.8
  ) +
  geom_line(
    data = fertility_ab,
    aes(
      x           = year,
      y           = fertility_rate * scale_factor,
      colour      = age_band,
      group       = age_band,
      legendgroup = age_band,
      text        = paste0(
        "Year: ", year,
        "<br>Age band: ", age_band,
        "<br>Fertility: ", round(fertility_rate,2)
      )
    ),
    size = 0.8,
    alpha = 0.8
  ) +
  geom_point(
    data = fertility_ab,
    aes(
      x      = year,
      y      = fertility_rate * scale_factor,
      colour = age_band,
      group  = age_band,
      legendgroup = age_band,
      text   = paste0(
        "Year: ", year,
        "<br>Age band: ", age_band,
        "<br>Fertility: ", round(fertility_rate,2)
      )
    ),
    size = 1
  ) +
  scale_y_continuous(
    name   = "Female Population (thousands)",
    labels = comma,
    sec.axis = sec_axis(
      transform = ~ . / scale_factor,
      name      = "Fertility Rate (per thousand females)",
      labels    = label_number(accuracy = 0.1)
    )
  ) +
  scale_fill_manual(
    name   = "Marital Status / Labour Status",
    values = c(
      "married / labour_force"                  = "#00CD00",  # green3
      "married / outside_labour_force"          = "#006400",  # darkgreen
      "single / labour_force"                   = "#EE0000",  # red2
      "single / outside_labour_force"           = "#8B0000",  # darkred
      "widowed_divorced / labour_force"         = "#0000FF",  # blue1
      "widowed_divorced / outside_labour_force" = "#00008B"   # blue4
    )
  ) +
  scale_colour_brewer(
    palette = "Set2",
    name    = "Age Band"
  ) +
  labs(
    title    = "Age‐Banded Fertility Rates vs Female Labour Counts",
    subtitle = "Bars by Marital & Labour Status; Lines by Age Band",
    x        = "Year",
    caption  = "Data: SingStat & data.gov.sg"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title       = element_text(face = "bold", size = 12),
    plot.subtitle    = element_text(size = 12, color = "gray60"),
    axis.title       = element_text(face = "bold", size = 6),
    legend.position  = "bottom",
    panel.grid.minor = element_blank()
  )

# 6. Convert to Plotly
interactive_ab <- ggplotly(p_ab, tooltip = "text") %>%
  layout(
    legend = list(
      orientation = "h",
      x           = 0.5,
      xanchor     = "center",
      y           = -0.25,
      font        = list(size = 8),
      title       = list(
        text = "Marital Status / Labour Status",
        font = list(size = 10)
      ),
      itemwidth   = 70
    ),
    margin = list(
      l = 80,    # left
      r = 80,    # right
      b = 140,   # bottom
      t = 80
    )
  )

# 7. Turn off built-in legends
for(i in seq_along(interactive_ab$x$data)) {
  interactive_ab$x$data[[i]]$showlegend <- FALSE
}

# 8. Define EXACT color mapping for status categories
status_colors <- c(
  "married / labour_force"                  = "#00CD00",  # green3
  "married / outside_labour_force"          = "#006400",  # darkgreen
  "single / labour_force"                   = "#EE0000",  # red2
  "single / outside_labour_force"           = "#8B0000",  # darkred
  "widowed_divorced / labour_force"         = "#0000FF",  # blue1
  "widowed_divorced / outside_labour_force" = "#00008B"   # blue4
)

status_labels <- c(
  "Married – In Labour Force",
  "Married – Outside Labour Force",
  "Single – In Labour Force",
  "Single – Outside Labour Force",
  "Divorced/Widowed – In Labour Force",
  "Divorced/Widowed – Outside Labour Force"
)

# 9. Create status legend using exact hex colors
status_traces <- lapply(1:6, function(i) {
  list(
    x = NA, 
    y = NA,
    name = status_labels[i],
    type = "scatter",
    mode = "markers",
    marker = list(
      color = status_colors[i],
      symbol = "square",
      size = 8
    ),
    showlegend = TRUE,
    hoverinfo = "none"  # Prevent hover info on legend items
  )
})

# 10. Create age-band legend with explicit colors
age_bands <- c("15-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49")
age_colors <- RColorBrewer::brewer.pal(length(age_bands), "Set2")

age_traces <- lapply(seq_along(age_bands), function(i) {
  list(
    x = NA, 
    y = NA,
    name = age_bands[i],
    type = "scatter",
    mode = "lines",
    line = list(color = age_colors[i], width = 2),
    showlegend = TRUE,
    hoverinfo = "none"  # Prevent hover info on legend items
  )
})

# 11. Append custom legend traces
interactive_ab$x$data <- c(
  interactive_ab$x$data,
  status_traces,
  age_traces
)

# 12. Adjust layout for better legend display
interactive_ab <- interactive_ab %>%
  layout(
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.3,  # Move legend lower
      font = list(size = 8),
      traceorder = "normal",  # Keep specified order
      itemsizing = "constant", # Consistent item sizes
      itemwidth = 30
    ),
    margin = list(
      l = 80,
      r = 80,
      b = 180,  # Increase bottom margin for legend space
      t = 80
    )
  )

# 13. Render final visualization
tagList(
  div(style = "margin-bottom:20px;",
      h3("Interactive Age‐Banded Fertility Dashboard"),
      p("Use the dropdown to filter bars by labour status.")
  ),
  filter_ab,
  interactive_ab
)

Interactive Age‐Banded Fertility Dashboard

Use the dropdown to filter bars by labour status.

9 Key Findings & Insights

9.1 Summary Statistics

9.2 Statistical Significance Testing

10 Team Contributions

```

References

Data.gov.sg. (n.d.a). Data.gov.sg. https://staging.data.gov.sg/datasets?query=household&page=1&searchColumns=Year&resultId=d_e19478b30d8f5cd6a1dc482bf2e46eb7
Data.gov.sg. (n.d.b). Data.gov.sg. https://staging.data.gov.sg/datasets?query=household&page=1&searchColumns=Year&resultId=d_e2475676af29ec78749f1b22cf8b301c
Statistics, S. D. of. (n.d.). Population by age group, sex and type of locality, 2023 [table M810091]. https://tablebuilder.singstat.gov.sg/table/TS/M810091
Tan, T. (2024a). Singapore’s total fertility rate hits record low in 2023, falls below 1 for first time. The Straits Times. https://www.straitstimes.com/singapore/politics/singapore-s-total-fertility-rate-hits-record-low-in-2023-falls-below-1-for-first-time
Tan, T. (2024b). Why the fertility rate doesn’t capture socio-economic or cultural trends. The Straits Times. https://www.straitstimes.com/singapore/why-the-fertility-rate-doesn-t-capture-socio-economic-or-cultural-trends